1,773 research outputs found
StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge
Today, massive amounts of streaming data from smart devices need to be
analyzed automatically to realize the Internet of Things. The Complex Event
Processing (CEP) paradigm promises low-latency pattern detection on event
streams. However, CEP systems need to be extended with Machine Learning (ML)
capabilities such as online training and inference in order to be able to
detect fuzzy patterns (e.g., outliers) and to improve pattern recognition
accuracy during runtime using incremental model training. In this paper, we
propose a distributed CEP system denoted as StreamLearner for ML-enabled
complex event detection. The proposed programming model and data-parallel
system architecture enable a wide range of real-world applications and allow
for dynamically scaling up and out system resources for low-latency,
high-throughput event processing. We show that the DEBS Grand Challenge 2017
case study (i.e., anomaly detection in smart factories) integrates seamlessly
into the StreamLearner API. Our experiments verify scalability and high event
throughput of StreamLearner.Comment: Christian Mayer, Ruben Mayer, and Majd Abdo. 2017. StreamLearner:
Distributed Incremental Machine Learning on Event Streams: Grand Challenge.
In Proceedings of the 11th ACM International Conference on Distributed and
Event-based Systems (DEBS '17), 298-30
Traffic System Anomaly Detection using Spatiotemporal Pattern Networks
Traffic dynamics in the urban interstate system are critical in terms of highway safety and mobility. This paper proposes a systematic data mining technique to detect traffic system-level anomalies in a batch-processing fashion. Built on the concepts of symbolic dynamics, a spatiotemporal pattern network (STPN) architecture is developed to capture the system characteristics. This novel spatiotemporal graphical modeling approach is shown to be able to extract salient time series features and discover spatial and temporal patterns for a traffic system. An information-theoretic metric is used to quantify the causal relationships between sub-systems. By comparing the structural similarity of the information-theoretic metrics of the STPNs learnt from each day, a day with anomalous system characteristics can be identified. A case study is conducted on an urban interstate in Iowa, USA, with 11 roadside radar sensors collecting 20-second resolution speed and volume data. After applying the proposed methods on one-month data (Feb. 2017), several system-level anomalies are detected. The potential causes that include inclement weather condition and non-recurring congestion are also verified to demonstrate the efficacies of the proposed technique. Compared to the traditional predefined performance measures for the traffic systems, the proposed framework has advantages in capturing spatiotemporal features in a fast and scalable manner
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Coreset Clustering on Small Quantum Computers
Many quantum algorithms for machine learning require access to classical data
in superposition. However, for many natural data sets and algorithms, the
overhead required to load the data set in superposition can erase any potential
quantum speedup over classical algorithms. Recent work by Harrow introduces a
new paradigm in hybrid quantum-classical computing to address this issue,
relying on coresets to minimize the data loading overhead of quantum
algorithms. We investigate using this paradigm to perform -means clustering
on near-term quantum computers, by casting it as a QAOA optimization instance
over a small coreset. We compare the performance of this approach to classical
-means clustering both numerically and experimentally on IBM Q hardware. We
are able to find data sets where coresets work well relative to random sampling
and where QAOA could potentially outperform standard -means on a coreset.
However, finding data sets where both coresets and QAOA work well--which is
necessary for a quantum advantage over -means on the entire data
set--appears to be challenging
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